Methods: Mind the Gap

Webinar Series

Joint Models of Longitudinal and Time-to-Event Data for Informing Multi-Stage Decision Making in mHealth

November 5, 2019
Dr. Dempsey
Walter Dempsey, Ph.D.

University of Michigan

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About the Webinar

Mobile health (mHealth) technologies are advancing at an accelerated pace, spurring rapid interest in digital monitoring and mHealth intervention development. Data arising from mHealth intervention studies is incredibly complex—mixed-type data collected at different time-scales with multiple intervention components (i.e., factors) each designed to impact health outcomes over a (potentially different) time-scale. Methods to (a) analyze this complex data and (b) evaluate validity and efficacy of the intervention components lag well behind scientific interest.

In this talk, Dr. Dempsey focuses on mHealth studies in which both longitudinal and time-to-event data are recorded per participant. From assessing levels of biomarker association with event risk, to defining risk strata for a stratified micro-randomized trial, to post-study analysis of the treatment effect on event risk, he discusses how joint models enter into various stages of the intervention development process. He also discusses how mHealth studies present novel methodological challenges for joint modeling and solutions in several case studies. In each instance, he connects the joint modeling perspective back to how scientists can use them to inform multi-stage decision making in mHealth.

About Walter Dempsey

Dr. Walter Dempsey is an Assistant Professor of Biostatistics at University of Michigan. His current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures, such as interaction networks. His research focuses on the design and application of novel statistical methodologies to make sense of complex longitudinal, survival, and relational datasets. This work informs decision making in health by aiding in intervention evaluation and development.